Intelligent Bio-Environments: Exploring Fuzzy Logic Approaches to the Honeybee Crisis

Bassford, Marie; Painter, Birgit

doi:10.1109/ie.2016.45

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…Information fusion refers to the process of combining data from different sensors or information sources to gain new or precise knowledge about physical quantities, events, or situations. Based on the abstraction level of information, information fusion methods can be categorized into three types: data-level fusion [20] , feature-level fusion [21] , and decision-level fusion [22] . Data-level fusion involves processing the original dataset, with fusion operations conducted before feature extraction.…”

Section: Information Fusionmentioning

confidence: 99%

Deep fusion network based on two-stream CNN for radar target recognition

丰怡,

Liu,

et al. 2024

MIPPR 2023: Pattern Recognition and Computer Vision

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Usually radar target recognition methods only use a single type of radar data, such as synthetic aperture radar (SAR) or high-resolution range profile (HRRP). Compared with SAR, HRRP lacks the azimuth distribution information of the scattering center, but it has much looser imaging conditions than SAR. Both of them are important for radar target recognition. In fact, there is a correspondence between them. Therefore, in this paper, we propose an end-to-end fusion network, which can make full use of the different characteristics obtained from HRRP data and SAR images. The proposed network can automatically extract the features of HRRP and SAR data for fusion target recognition. It is a dual stream structure, which contains two separate feature extraction streams. One stream uses a 1D CNN to extract the complex features of HRRP data for full angle domain recognition, and the other uses a multi-scale 2D CNN to extract SAR features. An adaptive fusion module is designed in this paper for deeply fusing the two stream features and output the final recognition results. The contributions of this method mainly include: (1) A new end-to-end HRRP/SAR fusion network is proposed, and the experiment shows that our network significantly improves the recognition accuracy; (2) In HRRP feature extraction flow, we use a 1d-CNN, which can extract full angle features; (3) A multi-scale convolution neural network is used for SAR image feature extraction, which can solve the scale imbalance problem of SAR.

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Section: Information Fusionmentioning

confidence: 99%

Deep fusion network based on two-stream CNN for radar target recognition

丰怡,

Liu,

et al. 2024

MIPPR 2023: Pattern Recognition and Computer Vision

View full text Add to dashboard Cite

show abstract

“…Information fusion can be categorized into three main types based on the level of abstraction of the information being fused: data-level fusion [26], feature-level fusion [27], and decision-level fusion [28].…”

Section: Information Fusionmentioning

confidence: 99%

SDRnet: A Deep Fusion Network for ISAR Ship Target Recognition Based on Feature Separation and Weighted Decision

Deng,

2024

Remote Sensing

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Existing methods for inverse synthetic aperture radar (ISAR) target recognition typically rely on a single high-resolution radar signal type, such as ISAR images or high-resolution range profiles (HRRPs). However, ISAR images and HRRP data offer representations of targets across different aspects, each containing valuable information crucial for radar target recognition. Moreover, the process of generating ISAR images inherently facilitates the acquisition of HRRP data, ensuring timely data collection. Therefore, to fully leverage the different information from both HRRP data and ISAR images and enhance ISAR ship target recognition performance, we propose a novel deep fusion network named the Separation-Decision Recognition network (SDRnet). First, our approach employs a convolutional neural network (CNN) to extract initial feature vectors from ISAR images and HRRP data. Subsequently, a feature separation module is employed to derive a more robust target representation. Finally, we introduce a weighted decision module to enhance overall predictive performance. We validate our method using simulated and measured data containing ten categories of ship targets. The experimental results confirm the effectiveness of our approach in improving ISAR ship target recognition.

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“…According to the abstract level of information, information fusion methods can be divided into three categories: data-level fusion [39], feature-level fusion [40] and decisionlevel fusion [41]. Data-level and decision-level fusion are the two most easily implemented information fusion methods, but their performance improvements are also limited.…”

Section: Information Fusionmentioning

confidence: 99%

Two-Stream Deep Fusion Network Based on VAE and CNN for Synthetic Aperture Radar Target Recognition

Guo

et al. 2021

Remote Sensing

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Usually radar target recognition methods only use a single type of high-resolution radar signal, e.g., high-resolution range profile (HRRP) or synthetic aperture radar (SAR) images. In fact, in the SAR imaging procedure, we can simultaneously obtain both the HRRP data and the corresponding SAR image, as the information contained within them is not exactly the same. Although the information contained in the HRRP data and the SAR image are not exactly the same, both are important for radar target recognition. Therefore, in this paper, we propose a novel end-to-end two stream fusion network to make full use of the different characteristics obtained from modeling HRRP data and SAR images, respectively, for SAR target recognition. The proposed fusion network contains two separated streams in the feature extraction stage, one of which takes advantage of a variational auto-encoder (VAE) network to acquire the latent probabilistic distribution characteristic from the HRRP data, and the other uses a lightweight convolutional neural network, LightNet, to extract the 2D visual structure characteristics based on SAR images. Following the feature extraction stage, a fusion module is utilized to integrate the latent probabilistic distribution characteristic and the structure characteristic for the reflecting target information more comprehensively and sufficiently. The main contribution of the proposed method consists of two parts: (1) different characteristics from the HRRP data and the SAR image can be used effectively for SAR target recognition, and (2) an attention weight vector is used in the fusion module to adaptively integrate the different characteristics from the two sub-networks. The experimental results of our method on the HRRP data and SAR images of the MSTAR and civilian vehicle datasets obtained improvements of at least 0.96 and 2.16%, respectively, on recognition rates, compared with current SAR target recognition methods.

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Intelligent Bio-Environments: Exploring Fuzzy Logic Approaches to the Honeybee Crisis

Cited by 7 publications

References 16 publications

Deep fusion network based on two-stream CNN for radar target recognition

Deep fusion network based on two-stream CNN for radar target recognition

SDRnet: A Deep Fusion Network for ISAR Ship Target Recognition Based on Feature Separation and Weighted Decision

Two-Stream Deep Fusion Network Based on VAE and CNN for Synthetic Aperture Radar Target Recognition

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